MESMERISED: Super-accelerating T1 relaxometry and diffusion MRI with STEAM at 7 T for quantitative multi-contrast and diffusion imaging

There is an increasing interest in quantitative imaging of T1, T2 and diffusion contrast in the brain due to greater robustness against bias fields and artifacts, as well as better biophysical interpretability in terms of microstructure. However, acquisition time constraints are a challenge, particularly when multiple quantitative contrasts are desired and when extensive sampling of diffusion directions, high b-values or long diffusion times are needed for multi-compartment microstructure modeling. Although ultra-high fields of 7 T and above have desirable properties for many MR modalities, the shortening T2 and the high specific absorption rate (SAR) of inversion and refocusing pulses bring great challenges to quantitative T1, T2 and diffusion imaging. Here, we present the MESMERISED sequence (Multiplexed Echo Shifted Multiband Excited and Recalled Imaging of STEAM Encoded Diffusion). MESMERISED removes the dead time in Stimulated Echo Acquisition Mode (STEAM) imaging by an echo-shifting mechanism. The echo-shift (ES) factor is independent of multiband (MB) acceleration and allows for very high multiplicative (ESxMB) acceleration factors, particularly under moderate and long mixing times. This results in super-acceleration and high time efficiency at 7 T for quantitative T1 and diffusion imaging, while also retaining the capacity to perform quantitative T2 and B1 mapping. We demonstrate the super-acceleration of MESMERISED for whole-brain T1 relaxometry with total acceleration factors up to 36 at 1.8 mm isotropic resolution, and up to 54 at 1.25 mm resolution qT1 imaging, corresponding to a 6x and 9x speedup, respectively, compared to MB-only accelerated acquisitions. We then demonstrate highly efficient diffusion MRI with high b-values and long diffusion times in two separate cases. First, we show that super-accelerated multi-shell diffusion acquisitions with 370 whole-brain diffusion volumes over 8 b-value shells up to b = 7000 s/mm2 can be generated at 2 mm isotropic in under 8 minutes, a data rate of almost a volume per second, or at 1.8 mm isotropic in under 11 minutes, achieving up to 3.4x speedup compared to MB-only. A comparison of b = 7000 s/mm2 MESMERISED against standard MB pulsed gradient spin echo (PGSE) diffusion imaging shows 70% higher SNR efficiency and greater effectiveness in supporting complex diffusion signal modeling. Second, we demonstrate time-efficient sampling of different diffusion times with 1.8 mm isotropic diffusion data acquired at four diffusion times up to 290 ms, which supports both Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI) at each diffusion time. Finally, we demonstrate how adding quantitative T2 and B1+ mapping to super-accelerated qT1 and diffusion imaging enables efficient quantitative multi-contrast mapping with the same MESMERISED sequence and the same readout train. MESMERISED extends possibilities to efficiently probe T1, T2 and diffusion contrast for multi-component modeling of tissue microstructure.

[1]  David G Norris,et al.  Power independent of number of slices (PINS) radiofrequency pulses for low‐power simultaneous multislice excitation , 2011, Magnetic resonance in medicine.

[2]  Robin M Heidemann,et al.  Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.

[3]  Tobias Kober,et al.  MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field , 2010, NeuroImage.

[4]  Mark F. Lythgoe,et al.  Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison , 2012, NeuroImage.

[5]  Jelle Veraart,et al.  TE dependent Diffusion Imaging (TEdDI) distinguishes between compartmental T 2 relaxation times , 2017, NeuroImage.

[6]  A. Lutti,et al.  A General Linear Relaxometry Model of R1 Using Imaging Data , 2014, Magnetic resonance in medicine.

[7]  Richard Bowtell,et al.  Echo-shifted multislice EPI for high-speed fMRI. , 2006, Magnetic resonance imaging.

[8]  Peter J. Koopmans,et al.  Application of PINS radiofrequency pulses to reduce power deposition in RARE/turbo spin echo imaging of the human head , 2014, Magnetic resonance in medicine.

[9]  Jelle Veraart,et al.  On the scaling behavior of water diffusion in human brain white matter , 2019, NeuroImage.

[10]  J. Polimeni,et al.  Blipped‐controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g‐factor penalty , 2012, Magnetic resonance in medicine.

[11]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[12]  Jelle Veraart,et al.  One diffusion acquisition and different white matter models: How does microstructure change in human early development based on WMTI and NODDI? , 2015, NeuroImage.

[13]  D. Alexander A general framework for experiment design in diffusion MRI and its application in measuring direct tissue‐microstructure features , 2008, Magnetic resonance in medicine.

[14]  R. Cusack,et al.  New Robust 3-D Phase Unwrapping Algorithms: Application to Magnetic Field Mapping and Undistorting Echoplanar Images , 2002, NeuroImage.

[15]  Brian Hansen,et al.  Diffusion time dependence of microstructural parameters in fixed spinal cord , 2017, NeuroImage.

[16]  Jelle Veraart,et al.  In vivo observation and biophysical interpretation of time-dependent diffusion in human white matter , 2016, NeuroImage.

[17]  D. Larkman,et al.  Use of multicoil arrays for separation of signal from multiple slices simultaneously excited , 2001, Journal of magnetic resonance imaging : JMRI.

[18]  T. Peters,et al.  High‐resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2 , 2005, Magnetic resonance in medicine.

[19]  D. LeBihan IVIM method measures diffusion and perfusion. , 1990, Diagnostic imaging.

[20]  Kawin Setsompop,et al.  Echo planar time‐resolved imaging (EPTI) , 2019, Magnetic resonance in medicine.

[21]  R J Ordidge,et al.  Measurement of T1 by echo-planar imaging and the construction of computer-generated images. , 1986, Physics in medicine and biology.

[22]  O. Josephs,et al.  Robust and Fast Whole Brain Mapping of the RF Transmit Field B1 at 7T , 2012, PloS one.

[23]  L. Axel,et al.  Rapid B1+ mapping using a preconditioning RF pulse with TurboFLASH readout , 2010, Magnetic resonance in medicine.

[24]  Klaus Scheffler,et al.  Efficient generation of T2* ‐weighted contrast by interslice echo‐shifting for human functional and anatomical imaging at 9.4 Tesla , 2015, Magnetic resonance in medicine.

[25]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[26]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[27]  Timothy Edward John Behrens,et al.  The CONNECT project: Combining macro- and micro-structure , 2013, NeuroImage.

[28]  E. Hahn,et al.  Spin Echoes , 2011 .

[29]  Kawin Setsompop,et al.  Simultaneous multislice excitation by parallel transmission , 2014, Magnetic resonance in medicine.

[30]  Gregory R. Lee,et al.  Diffusion‐prepared fast imaging with steady‐state free precession (DP‐FISP): A rapid diffusion MRI technique at 7 T , 2012, Magnetic resonance in medicine.

[31]  F. Ståhlberg,et al.  The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter , 2013, Magnetic Resonance Materials in Physics, Biology and Medicine.

[32]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[33]  Stephen M. Smith,et al.  Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.

[34]  Kawin Setsompop,et al.  Interslice leakage artifact reduction technique for simultaneous multislice acquisitions , 2014, Magnetic resonance in medicine.

[35]  S. Berman,et al.  Modeling conduction delays in the corpus callosum using MRI-measured g-ratio , 2019, NeuroImage.

[36]  Rainer Goebel,et al.  Robust and fast nonlinear optimization of diffusion MRI microstructure models , 2017, NeuroImage.

[37]  Nikolaus Weiskopf,et al.  Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation , 2013, Front. Neurosci..

[38]  Jesper L. R. Andersson,et al.  Maximum a posteriori estimation of diffusion tensor parameters using a Rician noise model: Why, how and but , 2008, NeuroImage.

[39]  Julien Cohen-Adad,et al.  Promise and pitfalls of g-ratio estimation with MRI , 2017, NeuroImage.

[40]  Joseph V. Hajnal,et al.  Complex diffusion-weighted image estimation via matrix recovery under general noise models , 2018, NeuroImage.

[41]  P. Basser,et al.  Diffusion tensor MR imaging of the human brain. , 1996, Radiology.

[42]  Influence of water compartmentation and heterogeneous relaxation on quantitative magnetization transfer imaging in rodent brain tumors , 2016, Magnetic resonance in medicine.

[43]  Steen Moeller,et al.  Simultaneous multislice multiband parallel radiofrequency excitation with independent slice‐specific transmit B1 homogenization , 2013, Magnetic resonance in medicine.

[44]  Benedikt A. Poser,et al.  Investigating the benefits of multi-echo EPI for fMRI at 7 T , 2009, NeuroImage.

[45]  Jens Frahm,et al.  Stimulated echo imaging , 1985 .

[46]  C. Beaulieu,et al.  The basis of anisotropic water diffusion in the nervous system – a technical review , 2002, NMR in biomedicine.

[47]  Robert Turner,et al.  Myelin and iron concentration in the human brain: A quantitative study of MRI contrast , 2014, NeuroImage.

[48]  F. J. Fritz,et al.  Fast quantification of uncertainty in non-linear diffusion MRI models for artifact detection and more power in group studies , 2019, bioRxiv.

[49]  Hui Zhang,et al.  Axon diameter mapping in the presence of orientation dispersion with diffusion MRI , 2011, NeuroImage.

[50]  E. Fieremans,et al.  Novel White Matter Tract Integrity Metrics Sensitive to Alzheimer Disease Progression , 2013, American Journal of Neuroradiology.

[51]  Derek K. Jones,et al.  The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study † , 2004, Magnetic resonance in medicine.

[52]  Derek K. Jones,et al.  Why diffusion tensor MRI does well only some of the time: Variance and covariance of white matter tissue microstructure attributes in the living human brain☆ , 2014, NeuroImage.

[53]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[54]  Els Fieremans,et al.  Revealing mesoscopic structural universality with diffusion , 2014, Proceedings of the National Academy of Sciences.

[55]  Robert Turner,et al.  Diffusion imaging in humans at 7T using readout‐segmented EPI and GRAPPA , 2010, Magnetic resonance in medicine.

[56]  P. Basser,et al.  Axcaliber: A method for measuring axon diameter distribution from diffusion MRI , 2008, Magnetic resonance in medicine.

[57]  P. Mansfield,et al.  High‐speed multislice T1 mapping using inversion‐recovery echo‐planar imaging , 1990, Magnetic resonance in medicine.

[58]  Kawin Setsompop,et al.  A low power radiofrequency pulse for simultaneous multislice excitation and refocusing , 2014, Magnetic resonance in medicine.

[59]  Yogesh Rathi,et al.  High‐resolution in vivo diffusion imaging of the human brain with generalized slice dithered enhanced resolution: Simultaneous multislice (gSlider‐SMS) , 2018, Magnetic resonance in medicine.

[60]  Yaniv Assaf,et al.  Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain , 2005, NeuroImage.

[61]  Nikolaus Weiskopf,et al.  Using high-resolution quantitative mapping of R1 as an index of cortical myelination , 2014, NeuroImage.

[62]  Robert Turner,et al.  A simple low‐SAR technique for chemical‐shift selection with high‐field spin‐echo imaging , 2010, Magnetic resonance in medicine.

[63]  Robert Turner,et al.  Quantitative T1 mapping using multi-slice multi-shot inversion recovery EPI , 2021, NeuroImage.

[64]  Derek K. Jones,et al.  Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter , 2016, NeuroImage.

[65]  Y. Assaf,et al.  Improved precision in CHARMED assessment of white matter through sampling scheme optimization and model parsimony testing , 2014, Magnetic resonance in medicine.

[66]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[67]  M. Fukunaga,et al.  Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast , 2010, Proceedings of the National Academy of Sciences.

[68]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[69]  Tobias Kober,et al.  Robust T1-Weighted Structural Brain Imaging and Morphometry at 7T Using MP2RAGE , 2014, PloS one.

[70]  S. Wells,et al.  Stimulated echo based mapping (STEM) of T1, T2, and apparent diffusion coefficient: validation and protocol optimization , 2018, Magnetic resonance in medicine.

[71]  Ben Jeurissen,et al.  T1 relaxometry of crossing fibres in the human brain , 2016, NeuroImage.

[72]  Ian Marshall,et al.  Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting. , 2019, Magnetic resonance imaging.

[73]  Steen Moeller,et al.  NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing , 2020, NeuroImage.

[74]  Himanshu Bhat,et al.  Reduction of across-run variability of temporal SNR in accelerated EPI time-series data through FLEET-based robust autocalibration , 2017, NeuroImage.

[75]  Dmitry S. Novikov,et al.  Mesoscopic structure of neuronal tracts from time-dependent diffusion , 2015, NeuroImage.

[76]  Pierre-Louis Bazin,et al.  MP2RAGEME: T1, T2 *, and QSM mapping in one sequence at 7 tesla , 2018, Human brain mapping.

[77]  Chun-Hung Yeh,et al.  MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation , 2019, NeuroImage.

[78]  Robin M Heidemann,et al.  Controlled aliasing in volumetric parallel imaging (2D CAIPIRINHA) , 2006, Magnetic resonance in medicine.

[79]  Tim B. Dyrby,et al.  Orientationally invariant indices of axon diameter and density from diffusion MRI , 2010, NeuroImage.

[80]  S.N. Sotiropoulos,et al.  High resolution whole brain diffusion imaging at 7T for the Human Connectome Project , 2015, NeuroImage.

[81]  S. Deoni,et al.  High‐resolution T1 mapping of the brain at 3T with driven equilibrium single pulse observation of T1 with high‐speed incorporation of RF field inhomogeneities (DESPOT1‐HIFI) , 2007, Journal of magnetic resonance imaging : JMRI.

[82]  Daan Christiaens,et al.  Integrated and efficient diffusion-relaxometry using ZEBRA , 2018, Scientific Reports.

[83]  Andrew G Webb,et al.  Quantitative assessment of the effects of high‐permittivity pads in 7 Tesla MRI of the brain , 2012, Magnetic resonance in medicine.

[84]  Jan Sijbers,et al.  Denoising of diffusion MRI using random matrix theory , 2016, NeuroImage.

[85]  P. Basser,et al.  New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter , 2004, Magnetic resonance in medicine.

[86]  J. Duerk,et al.  Magnetic Resonance Fingerprinting , 2013, Nature.

[87]  Y. Assaf,et al.  Diffusion Tensor Imaging (DTI)-based White Matter Mapping in Brain Research: A Review , 2007, Journal of Molecular Neuroscience.